Could LSA become a “Bifactor” model? Towards a model with general and group factors
作者：
Highlights：
• Some limitations of a method to semantically interpret LSA dimensions are tackled.
• The method produces an orthogonal non-latent space from the LSA original latent one.
• A limitation is that the non-latent space does not represent the common variance.
• A Bifactor Model inspired method introduces an additional common variance dimension.
• The corrections proposed outperforms the current Inbuilt-Rubric version.
摘要
•Some limitations of a method to semantically interpret LSA dimensions are tackled.•The method produces an orthogonal non-latent space from the LSA original latent one.•A limitation is that the non-latent space does not represent the common variance.•A Bifactor Model inspired method introduces an additional common variance dimension.•The corrections proposed outperforms the current Inbuilt-Rubric version.
论文关键词：Latent semantic analysis,Bifactor model,Distributional semantics,Inbuilt-Rubric method,Rotation,Text assessment
论文评审过程：Received 8 July 2018, Revised 22 April 2019, Accepted 22 April 2019, Available online 23 April 2019, Version of Record 28 April 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.055
